Scheduling Methodology Research Articles

A hybrid augmented Lagrangian multiplier method for the open pit mines long-term production scheduling problem optimization

To improve the quality of decision making in the mining operation, it is essential to find global optimum in problems with large dimensional scales. It is widely accepted that Long-term production scheduling (LTPS) problem is playing key role in mining projects to improve their performance by considering availability constraints while maximizing the project total profits during the period. Since production scheduling problems are NP-hard, there is need of improving scheduling methodologies to get good solution. This paper presents a hybrid model between augmented Lagrangian relaxation (ALR) and Genetic algorithm (GA) to solve the LTPS problem. We propose to apply the ALR method on the LTPS problem which to improve its performance speeding up the convergence and also, GA is used to update the Lagrangian multipliers. The results from case study show that the ALR method is effective in solving large-scale problem and generation a feasible solution then the traditional linearization method. Furthermore, the proposed hybrid strategy based on GA showed better performance in comparison to the available methods.

An integrated job release and scheduling approach on parallel machines: An application in electric wire-harness industry

This paper presents a new job release (JR) and scheduling methodology for one-stage parallel machines where sequence dependent setup times exist. A decision support system (DSS) based on job release is developed in order to enable the application of the methodology. First, mathematical programming models for both job release and job scheduling are devised. Then, due to the NP-hard nature of the problems, heuristics are proposed. As for the interaction between JR and scheduling, job scheduling is integrated with job release for the proposed heuristic solutions so that the capacity achievement provided by scheduling can be utilized for job release. In brief, product design characteristics oriented scheduling affects JR in the proposed approach. Moreover, value stream mapping (VSM) approach is used with the aim of stating the effect of the proposed methodology. Furthermore, the presented methodology was applied in a real life electric wire-harness production system. The application, based on 120-day production data, revealed that the proposed methodology provided 25% decrease in in-plant manufacturing lead time.

Nurse preference rostering using agents and iterated local search

This study presents an iterative local search (ILS) framework used within an agent-based nurse rostering system. This agent based system with the ILS (CNR-ILS) is used to improve nurse rosters with respect to nurse preferences. The system is a heuristic that considers a variety of nurse considerations including informal requested days off, preferences for days-of-the-week off, and preferences for specific numbers of consecutive days off. Since our implementation is not subject to the complexity of mathematical programs, it includes more preference considerations. The system is extensively tested in a federal hospital and is compared to both an integer programming solution from the literature and the scheduling methodology currently used at the test facility. When nurses are surveyed, the solutions found by this system are consistently favored over the solutions from the integer program and hospital’s current scheduling practice. Furthermore the variation of those ratings is significantly less.

Reactive Scheduling of Short-Term Crude Oil Operations under Uncertainties

In this paper, an effective reactive scheduling methodology for short-term crude oil operations has been developed to manage crude movements from ship unloading to distillation processing under various uncertainties. It contains a two-stage solving procedure for handling uncertainties such as shipping delay, crude mixture demand change, and tank unavailability. On the first stage, a deterministic schedule is initially obtained based on a continuous-time global event model using nominal data provided/projected at the beginning of each scheduling time horizon. On the second stage, indicator binary variables are implemented respectively under any occurrence of different uncertainties to collect information from the current deterministic schedule through defining “executed” tasks. On the basis of the information, rescheduling models are configured correspondingly under diverse uncertainty scenarios by combining the first-stage scheduling model and amendment constraints associated with those “executed” tasks. ...

Retail store scheduling for profit

In spite of its tremendous economic significance, the problem of sales staff schedule optimization for retail stores has received relatively scant attention. Current approaches typically attempt to minimize payroll costs by closely fitting a staffing curve derived from exogenous sales forecasts, oblivious to the ability of additional staff to (sometimes) positively impact sales. In contrast, this paper frames the retail scheduling problem in terms of operating profit maximization, explicitly recognizing the dual role of sales employees as sources of revenues as well as generators of operating costs. We introduce a flexible stochastic model of retail store sales, estimated from store-specific historical data, that can account for the impact of all known sales drivers, including the number of scheduled staff, and provide an accurate sales forecast at a high intra-day resolution. We also present solution techniques based on mixed-integer (MIP) and constraint programming (CP) to efficiently solve the complex mixed integer non-linear scheduling (MINLP) problem with a profit-maximization objective. The proposed approach allows solving full weekly schedules to optimality, or near-optimality with a very small gap. On a case-study with a medium-sized retail chain, this integrated forecasting–scheduling methodology yields significant projected net profit increases on the order of 2–3% compared to baseline schedules.

Additional Resource Allocation for improving Fairness in WiMAX

IEEE 802.16 standard provides resources to both service classes Constant Bit Rate (CBR) and Variable Bit Rate (VBR). Both of these services required enough resources to transmit data efficiently.Considering available literature there is a gap to provide additional resources for fulfillment of the required service class.In this paper we propose a Service Based Fair Resource Allocation (SBFRA) Mechansim, for evaluating the required service from Subscriber (SS) along with the channel condition. Our proposed model evaluates, for providing the required service to SS how much additional resource will be required. In this model we introduced Priority Queue Scheduling Methodology for providing additional resources as per channel condition. In this paper we made comparison with and without proposed model on both traffic, CBR and VBR. We experiment that our proposed model manages the user request by providing them additional resources as required for satisfaction. Their request handled in Priority Queue Scheduling based mechanism. Results shows, that we achieve improvement by providing the additional resource on fair scheduling basis. To achieve the required performance for fairness we have to compromise on with delay.

Additional Resource Allocation for improving Fairness in WiMAX

IEEE 802.16 standard provides resources to both service classes Constant Bit Rate (CBR) and Variable Bit Rate (VBR). Both of these services required enough resources to transmit data efficiently.Considering available literature there is a gap to provide additional resources for fulfillment of the required service class.In this paper we propose a Service Based Fair Resource Allocation (SBFRA) Mechansim, for evaluating the required service from Subscriber (SS) along with the channel condition. Our proposed model evaluates, for providing the required service to SS how much additional resource will be required. In this model we introduced Priority Queue Scheduling Methodology for providing additional resources as per channel condition. In this paper we made comparison with and without proposed model on both traffic, CBR and VBR. We experiment that our proposed model manages the user request by providing them additional resources as required for satisfaction. Their request handled in Priority Queue Scheduling based mechanism. Results shows, that we achieve improvement by providing the additional resource on fair scheduling basis. To achieve the required performance for fairness we have to compromise on with delay.

Smoothing Short Duration Capacity Problems in Enterprise Resource Planning (ERP) Systems: An Algorithmic Approach

Successful implementation of ERP systems should take full advantage of the access to information, but not be constrained by many of the deficiencies associated with infinite capacity scheduling methodologies. In this paper an algorithm is developed which improves the performance of the ERP system. Lot sizing decisions based on capacity availability are used as an instrument to integrate more effectively capacity requirements planning (CRP) and Material Requirements Planning (MRP). MRP is a planning tool for a sub-set of manufacturing system specially in hierarchal multi-product, multi-period and multi-stage production planning and inventory control system. The Vehicle used for this integration is planned order release (POR) quantity. The algorithm requires minimal deviation from the MRP logic. In this sequential process MRP first issues the action notices and then the algorithm analyses the capacity situation in the work centers. The algorithm scans all planned order release quantities of lower level items in the action bucket before orders are released to the shop. For each item a delta value is computed identifying the degree orders are contributing to capacity problems. This delta is specific to each POR. The POR causing the highest overload and the one passing through the lowest utilized work centers are selected for a lot size decrease, respectively, an increase. After identifying the candidates, a series of checks analyses the suitability of the candidates for that change. The planned order release quantities of more items can be performed. Projected capacity profile after iteration will be updated and capacity requirements over a short duration are smoothed. The experiments are performed with MAP/3000 as a simulation model for this study. The developed algorithm is added to MAP/3000 as additional subroutines. The design of the experiments consists of a base test identifying the significance of different environmental parameters of MRP systems and a main test which performs a detailed evaluation. The results are evaluated using analysis of variance techniques. Most ERP systems built on the historical development of MRP and CRP systems, and the assumption of infinite capacity is affecting the performance of those systems badly. This paper is presenting an algorithm to smooth capacity problems by using the existed capacity.

Multiobjective Augmented Eps-Constraint Optimization for Economic/Environmental Stochastic Unit Commitment in Power Systems with High Penetration of Wind Power

To regard environmental protection renewable energy sources especially wind, has been applied to achieve emission reduction goals. While wind generation does not directly produce air pollutants emission, it causes some changes on thermal power generation scheduling which may lead them to produce more air pollutants emission especially during low and medium energy demand periods. So it seems necessary to consider air pollutants emission level in wind-thermal scheduling problems. This paper proposes a methodology for wind-thermal scheduling in a power system with high penetration of wind power subject to consider air pollutants emission reduction. Because of simultaneous minimizing total operating cost and air pollutants emission, a Multiobjective Mathematical Programming (MMP) is introduced. The computation of the required reserve levels and their costs is attained through a stochastic programming market-clearing model. Also, the network constraints and the costs of both the load shedding and the wind spillage are considered. The MMP approach based on Augmented Eps-constraint algorithm has been suggested for generating Pareto-optimal solutions of wind-thermal units scheduling problem. Additionally, Fuzzy decision making process is employed to choose one of the Pareto-optimal solutions as the best compromise nondominated solution. The usefulness of the proposed approach were demonstrated through an IEEE 30-bus test system

Stiction Quantification: A Robust Methodology for Valve Monitoring and Maintenance Scheduling

Valve stiction is one of the most common causes of poor performance in control loops. This paper presents a procedure which allows stiction quantification. The technique permits one to estimate the unknown real stem position, and moreover, it does not need any process knowledge and requires only the data normally registered in industrial plants. It is pointed out that the real problem consists of the lack of knowledge about the true value of stiction. A general methodology is proposed to discard data for which quantification is very likely to give wrong indications and to restrict its application to appropriate cases. Simulations show that several sources of perturbations can be eliminated, thus improving the reliability of stiction evaluation. Results are confirmed by application to industrial data: a significant number of valves are analyzed for repeated acquisitions before and after plant shutdown. The proposed procedure seems to be a valid methodology to monitor valve stiction and to schedule and check valve maintenance.

Earned readiness management for scheduling, monitoring and evaluating the development of complex product systems

How should the development of a complex product system (CPS) be managed in a manner that focuses on process milestones, which is responsive to changes in technology and requirements; based on maturity measures; and applied in an interactive manner, in addition to facilitating timely feedback? This is considered to be an important question in project management. Project management tools and techniques have been inadequate for monitoring technology development in a CPS. If the technologies are not properly matured by a specific period of time, the progress of the project can be in detriment. To address this important gap, the objective of this study is to develop a new maturity-focused methodology for scheduling, monitoring and evaluating the development of a system. We present Earned Readiness Management (ERM) for system scheduling, monitoring and evaluation which was developed and validated using a case study. Future research on ERM is also discussed in this paper.

A Fuzzy Scheduling Methodology for Energy R&D Projects

The efficient management of energy research and development (R&D) projects is important to reduce the required the development time and cost. However, each development project is unique and innovational in nature and the duration of activities involved in an energy R&D project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this paper is to develop a fuzzy scheduling methodology to deal with these problems. Fuzzy variables theory is used to model the uncertain and flexible temporal information. A fuzzy scheduling algorithm with depth-first search is developed to find the possible critical paths based on fuzzy expected value simulation. A numerical example with solar photovoltaics development project is used to illustrate the effectiveness of the methodology.

Designing Stipulated Gains of Aircraft Stability and Control Augmentation Systems for Semiglobal Trajectories Tracking

The main objective of the current investigation is to provide a simple procedure to select the controller gains for an aircraft with a largely wide complex flight envelope with different source of nonlinearities. The stability and control gains are optimally devised using genetic algorithm. Thus, the gains are tuned based on the information of a single designed mission. This mission is assigned to cover a wide range of the aircraft’s flight envelope. For more validation, the resultant controller gains were tested for many off-designed missions and different operating conditions such as mass and aerodynamic variations. The results show the capability of the proposed procedure to design a semiglobal robust stability and control augmentation system for a highly maneuverable aircraft such as F-16. Unlike the gain scheduling and other control design methodologies, the proposed technique provides a semi-global single set of gains for both aircraft stability and control augmentation systems. This reduces the implementation efforts. The proposed methodology is superior to the classical control method which rigorously requires the linearization of the nonlinear aircraft model of the investigated highly maneuverable aircraft and eliminating the sources of nonlinearities mentioned above.

Resource preprocessing and optimal task scheduling in cloud computing environments

SummaryCloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Production Planning and Process Improvement in an Impeller Manufacturing Using Scheduling and OEE Techniques

In today's scenario, planning and scheduling methodologies influence the improvement in delivery performance of any manufacturing organization. In addition, quick changeover technique also helps in reduction of setup time of standardized products through Overall Equipment Effectiveness (OEE) technique. A study was carried out to improve the production planning and process improvement in an impeller manufacturing plant. The approach involved reviewing the present planning and scheduling methodologies, identifying bottlenecks, changeover procedures and reducing work-in-progress (WIP). The present study resulted in delivery performance improvement by 4.4% and47% reduction in set-up times in particular impellers.

Use of Metamodels in Real-Time Operation of Water Distribution Systems

This paper presents a methodology for real-time pump scheduling in water distribution system. The methodology uses an optimizer linked to other two modules: one to predict the hydraulic behavior of the system and another to forecast the demands. A metamodel is used instead of a traditional hydraulic simulator to speed up relevant computations, i.e. evaluations of large number of candidate pump schedules generated during the optimization process. The methodology presented is applied to a real-life water distribution system in Brazil. The results obtained show that the use of metamodel can generate low cost pump schedules in a computationally fast manner.

Security-Aware Packet Scheduling Scheme with Multi-Level Queuing and RSA

Emerging security-aware packet scheduling algorithms can efficiently improve the security measures while forwarding the packets over wireless links. Existing scheduling algorithms for real-time wireless networks are not capable of providing higher level of security. The proposed methodology overcomes the difficulties in the existing scheduling algorithm with the security enhancements. A multilevel queue scheduling algorithm is used to partition the ready queue into several separate queues.For security reasons, the proposed methodology uses the RSA algorithm for encrypting and decrypting the packets. The proposed Security-aware Packet Scheduling (SPS) methodology strives to improve the security levels while achieving high schedulability for real-time packets. The proposed model reduces the congestion occurred during scheduling the packets.The performance of the proposed method results better security than the existing Quality Based Searching algorithm. Also, the system takes lesser time, reduces congestion and traffic also provides higher arrival rate for scheduling the packets. Index Terms: Encryption, Decryption, Multilevel queue scheduling, RSA, Real-time packets, andScheduling algorithms

Robust Spatial Reuse Scheduling in Underwater Acoustic Communication Networks

Resource assignment in underwater acoustic communication (UWAC) networks has recently drawn much attention in the research community. Although in most applications the number of nodes in the UWAC network is relatively small, the long propagation delay of acoustic signals underwater motivates the application of spatial reuse in channel access protocols for throughput enhancement. In this paper, we address the problem of spatial-reuse scheduling in UWAC networks that support frequent transmission of broadcast packets and require robustness to inaccurate topology information. Taking the possibility of outdated network topology information into account is of great importance for UWAC applications due to time-varying topologies in the underwater environment. Our main contribution is the derivation of a broadcast scheduling algorithm that combines topology-transparent and topology-dependent spatial-reuse scheduling methodologies to achieve high throughput in static and dynamic topology scenarios. Simulation results demonstrate that our protocol provides a favorable tradeoff between network throughput and robustness to outdated topology information due to topology changes, and that it also achieves fairness in terms of per-node throughput.

Automated Productivity-Based Schedule Animation: Simulation-Based Approach to Time-Cost Trade-Off Analysis

AbstractMany studies of construction labor productivity are at a macro level. These macrolevel studies attempt to quantify the productivity or changes in productivity over the entire industry or a segment of the industry. This study is aimed at a micro or project level examination of productivity. The tool developed allows a company to predict, manage, and even optimize the productivity associated with a project or levels of projects. Automated productivity-based schedule animation (APBSA) is a dynamic scheduling methodology that stochastically utilizes weekly trended construction labor productivity data in order to automatically update the baseline schedule that was created at the beginning of the project by the construction manager. APBSA reports the construction activity duration variances and animates the progress of the job with most current data available in a four-dimensional (4D) environment. Contractors can improve their planning by utilizing APBSA with building information modeling (BIM) which w...

Comparative Study of Well Known Sequencing Methodologies and Differential Evolution: A Case Study of Thailand Garment Industries

The objective of this research is to study the problem and efficiency improvement of the clean room garment factory. The methods of production schedule were performed by heuristic technique of well-known scheduling methodologies including Shortest Processing Time, , Earliest Due Date, First Come First Serve method and Differential Evolution Method (DE). All methods can help to reduce the costs of delay shipment and time loss in various case studies. They were applied to the production order in 2 months after flooding problems in Thailand .The demand has a lot of quantities over production capacity. The results showed that the rules of Earliest Due Date method (EDD) is a powerful methodology for increasing production efficiency and minimizing make span. Whereas, the First Come First Serve (FCFS) method is the best method for finding a minimum value of total late job. Finally, Differential Evolution Method (DE) is the best method for finding a minimize value of average tardiness job.